week 8 lab hw
Jeremy Wadowski
March 1, 2017
#http://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html#org93999d8
housing <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/landdata-states.csv")
head(housing[1:5])## State region Date Home.Value Structure.Cost
## 1 AK West 2010.25 224952 160599
## 2 AK West 2010.50 225511 160252
## 3 AK West 2009.75 225820 163791
## 4 AK West 2010.00 224994 161787
## 5 AK West 2008.00 234590 155400
## 6 AK West 2008.25 233714 157458#Base graphics histogram example
hist(housing$Home.Value)
## ggplot2 histogram example
library(ggplot2)
ggplot(housing, aes(x = Home.Value)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Base colored scatter plot example
plot(Home.Value ~ Date,
data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
data=subset(housing, State == "TX"))
legend(1975, 400000,
c("MA", "TX"), title="State",
col=c("black", "red"),
pch=c(1, 1))
## ggplot2 colored scatter plot example
ggplot(subset(housing, State %in% c("MA", "TX")),
aes(x=Date,
y=Home.Value,
color=State))+
geom_point()
# Geometric Objects and Aesthetic
hp2001Q1 <- subset(housing, Date == 2001.25)
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = Land.Value)) +
geom_point()
hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))
p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))
p1 + geom_point(aes(color = Home.Value)) +
geom_line(aes(y = pred.SC))
p1 +
geom_point(aes(color = Home.Value)) +
geom_smooth()## `geom_smooth()` using method = 'loess'
p1 +
geom_text(aes(label=State), size = 3)
#install.packages("ggrepel")
library("ggrepel")
p1 +
geom_point() +
geom_text_repel(aes(label=State), size = 3)
p1 +
geom_point(aes(size = 2),# incorrect! 2 is not a variable
color="red") # this is fine -- all points red
p1 +
geom_point(aes(color=Home.Value, shape = region))## Warning: Removed 1 rows containing missing values (geom_point).
# Exercise 1
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asiaggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + geom_point()
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point()
ggplot(dat, aes(x = CPI, y = HDI), color="blue") + geom_point()
ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank, color=Region)) + geom_point()
# Setting Statistical Transformation Arguments
args(geom_histogram)## function (mapping = NULL, data = NULL, stat = "bin", position = "stack",
## ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA,
## inherit.aes = TRUE)
## NULLargs(stat_bin)## function (mapping = NULL, data = NULL, geom = "bar", position = "stack",
## ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL,
## breaks = NULL, closed = c("right", "left"), pad = FALSE,
## na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
## NULLp2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#with changed bin value
p2 + geom_histogram(stat = "bin", binwidth=4000)
#Changing the Statistical Transformation
housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))## State Home.Value
## 1 AK 147385.14
## 2 AL 92545.22
## 3 AR 82076.84
## 4 AZ 140755.59
## 5 CA 282808.08
## 6 CO 158175.99
## 46 VA 155391.44
## 47 VT 132394.60
## 48 WA 178522.58
## 49 WI 108359.45
## 50 WV 77161.71
## 51 WY 122897.25#Doesn't work because we are asking ggplot to bin and summarize already binned and summarized data
#ggplot(housing.sum, aes(x=State, y=Home.Value)) + geom_bar()
#instead:
ggplot(housing.sum, aes(x=State, y=Home.Value)) +
geom_bar(stat="identity")
# Exercise 2
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
head(dat)## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asiaggplot(dat, aes(x = CPI, y = HDI))+ geom_point()
ggplot(dat, aes(x = CPI, y = HDI))+ geom_point() + geom_smooth()## `geom_smooth()` using method = 'loess'
c(method="lm")## method
## "lm"ggplot(dat, aes(x = CPI, y = HDI))+ geom_point() + geom_line(stat="identity")
ggplot(dat, aes(x = CPI, y = HDI))+ geom_point() + geom_smooth(span = .2)## `geom_smooth()` using method = 'loess'
## Scale Modification Examples
p3 <- ggplot(housing,
aes(x = State,
y = Home.Price.Index)) +
theme(legend.position="top",
axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
alpha = 0.5,
size = 1.5,
position = position_jitter(width = 0.25, height = 0)))
p4 + scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"))
p4 +
scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue", high = "red")
#p4 +
#scale_color_continuous(name="",
#breaks = c(1976, 1994, 2013),
#labels = c("'76", "'94", "'13"),
#low = muted("blue"), high = muted("red")) #does not work with current package, requires scale package
#midpoint and different color scales
p4 +
scale_color_gradient2(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = ("blue"),
high = ("red"),
mid = "gray60",
midpoint = 1994)
## Exercise 3
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point()
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
scale_color_discrete(name = "World Regions")
ggplot(dat, aes(x = CPI, y = HDI, color = "Region")) +
geom_point() +
scale_x_continuous(name = "Corruption Perception Index") +
scale_y_continuous(name = "Human Development Index") +
scale_color_manual(name = "Region of the world",
values = c("#24576D",
"#099DD7",
"#28AADC",
"#248E84",
"#F2583F",
"#96503F")) #no different colors?? 
## Faceting
p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State)) #produces obscure lines
(p5 <- p5 + geom_line() +
facet_wrap(~State, ncol = 10))
## Themes
p5 + theme_linedraw()
p5 + theme_light()
p5 + theme_minimal() +
theme(text = element_text(color = "turquoise"))
#creating new themes
theme_new <- theme_bw() +
theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
text=element_text(size = 12, family = "Serif", color = "ivory"),
axis.text.y = element_text(colour = "purple"),
axis.text.x = element_text(colour = "red"),
panel.background = element_rect(fill = "pink"),
strip.background = element_rect(fill = ("orange")))
p5 + theme_new
## Best way to plot a data.frame of two variables as separate points with different colors?
#not this way:
library(tidyr)
housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear,
aes(x=Date)) +
geom_line(aes(y=Home.Value), color="red") +
geom_line(aes(y=Land.Value), color="blue")
#but this way:
home.land.byyear <- gather(housing.byyear,
value = "value",
key = "type",
Home.Value, Land.Value)
ggplot(home.land.byyear,
aes(x=Date,
y=value,
color=type)) +
geom_line()
# Putting It All Together: Recreate Economist Graph
dat <- read.csv("/home/eeb177-student/Desktop/eeb-177/lab-work/exercise-8/Rgraphics/dataSets/EconomistData.csv")
first <- ggplot(dat, aes(x = CPI, y = HDI, color = Region))
first + geom_point()
(second <- first + geom_smooth(aes(group = 1), method = "lm", formula = y ~ log(x), se = FALSE, color = "red")) + geom_point()
(third <- second + geom_point(size = 1, shape = 1) + geom_point(size = 1, shape = 1) + geom_point(size = 1, shape = 1))
labeled_countries <- c("Congo", "Afghanistan", "Sudan", "Myanmar", "Iraq", "India", "Rwanda", "South Africa", "China", "Venezuela", "Russia", "Argentina", "Greece", "Brazil", "Italy", "Bhutan", "Cape Verde", "Botswana", "Spain", "France", "US", "Britain", "Barbados", "Japan", "Germany", "Norway", "Singapore", "New Zealand")
dat$Country <- as.character(dat$Country)
as.character(dat$Country) %in% labeled_countries## [1] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
## [34] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [78] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [100] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [111] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [122] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [144] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [155] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [166] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSEcountry_to_print <- character(nrow(dat))
printrows <- which(as.character(dat$Country) %in% labeled_countries)
#country_to_print[printrows] <- #dat$Country[printrows]
library("ggrepel")
(fourth <- third + geom_text_repel(aes(label = Country), color = "black", data = subset(dat, Country%in%labeled_countries), force = 6))
library(grid)
(fifth <- fourth + ggtitle("Corruption and human development") + scale_x_continuous(name = "Corruption Perceptions Index, 2011 (10=least corrupt)", limits = c(.9, 10.5), breaks = 1:10) + scale_y_continuous(name = "Human Development Index, 2011 (1=Best)", limits = c(0.2, 1.0), breaks = seq(0.2, 1.0, by = 0.1)))
(sixth <- fifth + theme_minimal() + theme(text = element_text(color = "black"), legend.position = c("top"), legend.direction = "horizontal", legend.justification = 0.25, legend.text = element_text(size = 8, color = "black"), axis.text = element_text(face = "italic", size = 5), axis.ticks.y = element_blank(), axis.line = element_line(color = "black", size = 0.4),axis.line.y =element_blank(), panel.grid.major = element_line(color = "black", size = 0.4),panel.grid.major.x = element_blank()))